Recent advances in multi-agent systems (MAS) enable tools for information search and ideation by assigning personas to agents. However, how users can effectively control, steer, and critically evaluate collaboration among multiple domain-expert agents remains underexplored. We present Perspectra, an interactive MAS that visualizes and structures deliberation among LLM agents via a forum-style interface, supporting @-mention to invite targeted agents, threading for parallel exploration, with a real-time mind map for visualizing arguments and rationales. In a within-subjects study with 18 participants, we compared Perspectra to a group-chat baseline as they developed research proposals. Our findings show that Perspectra significantly increased the frequency and depth of critical-thinking behaviors, elicited more interdisciplinary replies, and led to more frequent proposal revisions than the group chat condition. We discuss implications for designing multi-agent tools that scaffold critical thinking by supporting user control over multi-agent adversarial discourse.

Ranking product recommendations to optimize for a high click-through rate (CTR) or for high conversion, such as add-to-cart rate (ACR) and Order-Submit-Rate (OSR, view-to-purchase conversion) are standard practices in e-commerce. Optimizing for CTR appears like a straightforward choice: Training data (i.e., click data) are simple to collect and often available in large quantities. Additionally, CTR is used far beyond e-commerce, making it a generalist, easily implemented option. ACR and OSR, on the other hand, are more directly linked to a shop's business goals, such as the Gross Merchandise Value (GMV). In this paper, we compare the effects of using either of these objectives using an online A/B test. Among our key findings, we demonstrate that in our shops, optimizing for OSR produces a GMV uplift more than five times larger than when optimizing for CTR, without sacrificing new product discovery. Our results also provide insights into the different feature importances for each of the objectives.

When the feedback is not informative enough, the target However, in practice, ReL has been successfully applied reward is only partially identifiable, i.e., there only to IL (Ho & Ermon, 2016) and reward design (Christiano exists a set of rewards (the feasible set) that are et al., 2017). The most significant issue that prevents equally-compatible with the feedback. In this paper, the use of ReL algorithms to other applications is partial we show that there exists a choice of reward, identifiability (Cao et al., 2021; Kim et al., 2021; Skalse non-necessarily contained in the feasible set that, et al., 2023b). Indeed, the target reward may not be uniquely depending on the ReL application, improves the determined from the given feedback, but there is a set of reward performance w.r.t.

Sensemaking in unfamiliar domains can be challenging, demanding considerable user effort to compare different options with respect to various criteria. Prior research and our formative study found that people would benefit from reading an overview of an information space upfront, including the criteria others previously found useful. However, existing sensemaking tools struggle with the "cold-start" problem -- it not only requires significant input from previous users to generate and share these overviews, but such overviews may also turn out to be biased and incomplete. In this work, we introduce a novel system, Selenite, which leverages Large Language Models (LLMs) as reasoning machines and knowledge retrievers to automatically produce a comprehensive overview of options and criteria to jumpstart users' sensemaking processes. Subsequently, Selenite also adapts as people use it, helping users find, read, and navigate unfamiliar information in a systematic yet personalized manner. Through three studies, we found that Selenite produced accurate and high-quality overviews reliably, significantly accelerated users' information processing, and effectively improved their overall comprehension and sensemaking experience.

Literacy, or the ability to read and write, is a crucial indicator of success in life and greater society. It is estimated that 85% of people in juvenile delinquent systems cannot adequately read or write, that more than half of those with substance abuse issues have complications in reading or writing and that two-thirds of those who do not complete high school lack proper literacy skills. Furthermore, young children who do not possess reading skills matching grade level by the fourth grade are approximately 80% likely to not catch up at all. Many may believe that in a developed country such as the United States, literacy fails to be an issue; however, this is a dangerous misunderstanding. Globally an estimated 1.19 trillion dollars are lost every year due to issues in literacy; in the USA, the loss is an estimated 300 billion. To put it in more shocking terms, one in five American adults still fail to comprehend basic sentences. Making matters worse, the only tools available now to correct a lack of reading and writing ability are found in expensive tutoring or other programs that oftentimes fail to be able to reach the required audience. In this paper, our team puts forward a new way of teaching English spelling and word recognitions to grade school students in the United States: Wordification. Wordification is a web application designed to teach English literacy using principles of linguistics applied to the orthographic and phonological properties of words in a manner not fully utilized previously in any computer-based teaching application.

The Geometric Algebra Transformer (GATr) is a versatile architecture for geometric deep learning based on projective geometric algebra. We generalize this architecture into a blueprint that allows one to construct a scalable transformer architecture given any geometric (or Clifford) algebra. We study versions of this architecture for Euclidean, projective, and conformal algebras, all of which are suited to represent 3D data, and evaluate them in theory and practice. The simplest Euclidean architecture is computationally cheap, but has a smaller symmetry group and is not as sample-efficient, while the projective model is not sufficiently expressive. Both the conformal algebra and an improved version of the projective algebra define powerful, performant architectures.

Artificial intelligence (AI) has become an increasingly popular technology in recent years, with many businesses turning to AI to enhance their operations, improve customer experiences, and drive innovation. However, the prospect of using AI can be daunting for many business owners, particularly those who are unfamiliar with the technology. In this blog post, we'll outline some key steps that businesses can take to effectively leverage AI for their operations. Before you start implementing AI, it's important to identify your business goals. Ask yourself: what are the specific problems or challenges that AI can help you solve? Are you looking to automate certain tasks, improve customer service, or gain insights into your business data?

Originally published on Towards AI the World's Leading AI and Technology News and Media Company. If you are building an AI-related product or service, we invite you to consider becoming an AI sponsor. At Towards AI, we help scale AI and technology startups. Let us help you unleash your technology to the masses. During the application process for an AI-based company, I was given a take-home assessment that included a machine learning task.

The use of artificial intelligence in labeling and annotating data is a new trend that can help organizations create labels and metadata for their datasets. However, it can be a challenging task to find the best AI data labeling and data annotation services. One of the most popular reasons for this is that there are many providers of such services in the market including freelancers and companies. Data labeling and data annotation are two terms that are often used interchangeably, but they actually have different meanings. Data labeling is the process of assigning labels to data points so that they can be easily identified and categorized.

As a novice it does seem hard and difficult to get the algorithms right at first, some are simply better than others. In this article, we will look at many ways a person can choose an algorithm that can perform the best and give better results. As the first step you should know what the problem is. If you do not know your problem, then it will be difficult to choose an algorithm that works. Well, how can I identify the problem? Let us see there are many types of problems in machine learning, and you just need to ask simple questions and then you are on the right track.